Today

Access and work with station weather data from Global Historical Climate Network (GHCN)
Explore options for plotting timeseries
Trend analysis
Compute Climate Extremes

Climate Extremes

Climate Metrics: ClimdEX

Indices representing extreme aspects of climate derived from daily data:

climdex

Climate Change Research Centre (CCRC) at University of New South Wales (UNSW) (climdex.org). Same as included in NCO tools from last week.

27 Core indices

For example:

FD Number of frost days: Annual count of days when TN (daily minimum temperature) < 0C.
SU Number of summer days: Annual count of days when TX (daily maximum temperature) > 25C.
ID Number of icing days: Annual count of days when TX (daily maximum temperature) < 0C.
TR Number of tropical nights: Annual count of days when TN (daily minimum temperature) > 20C.
GSL Growing season length: Annual (1st Jan to 31st Dec in Northern Hemisphere (NH), 1st July to 30th June in Southern Hemisphere (SH)) count between first span of at least 6 days with daily mean temperature TG>5C and first span after July 1st (Jan 1st in SH) of 6 days with TG<5C.
TXx Monthly maximum value of daily maximum temperature
TN10p Percentage of days when TN < 10th percentile
Rx5day Monthly maximum consecutive 5-day precipitation
SDII Simple pricipitation intensity index

Weather Data

Climate Data Online

CDO

GHCN

ghcn

Options for downloading data

`FedData` package

National Elevation Dataset digital elevation models (1 and 1/3 arc-second; USGS)
National Hydrography Dataset (USGS)
Soil Survey Geographic (SSURGO) database
International Tree Ring Data Bank.
Global Historical Climatology Network (GHCN)

NOAA API

noaa api

National Climatic Data Center application programming interface (API).

`rNOAA` package

Handles downloading data directly from NOAA APIv2.

buoy_* NOAA Buoy data from the National Buoy Data Center
ghcnd_* GHCND daily data from NOAA
isd_* ISD/ISH data from NOAA
homr_* Historical Observing Metadata Repository
ncdc_* NOAA National Climatic Data Center (NCDC)
seaice Sea ice
storm_ Storms (IBTrACS)
swdi Severe Weather Data Inventory (SWDI)
tornadoes From the NOAA Storm Prediction Center

Libraries

library(raster)
library(sp)
library(rgdal)
library(ggplot2)
library(ggmap)
library(dplyr)
library(tidyr)
library(maps)
# New Packages
library(rnoaa)
library(climdex.pcic)
library(zoo)
library(reshape2)

Station locations

Download the GHCN station inventory with ghcnd_stations().

datadir="data"

st = ghcnd_stations()$data

## Optionally, save it to disk
# write.csv(st,file.path(datadir,"st.csv"))
## If internet fails, load the file from disk using:
# st=read.csv(file.path(datadir,"st.csv"))

GHCND Variables

5 core values:

PRCP Precipitation (tenths of mm)
SNOW Snowfall (mm)
SNWD Snow depth (mm)
TMAX Maximum temperature
TMIN Minimum temperature

And ~50 others! For example:

ACMC Average cloudiness midnight to midnight from 30-second ceilometer
AWND Average daily wind speed
FMTM Time of fastest mile or fastest 1-minute wind
MDSF Multiday snowfall total

`filter()` to temperature and precipitation

st=dplyr::filter(st,element%in%c("TMAX","TMIN","PRCP"))

Map GHCND stations

First, get a global country polygon

worldmap=map("world",fill=T,plot=F)

Plot all stations:

ggplot() +
  facet_wrap(~element)+
  geom_point(aes(y=latitude,x=longitude),data=st,size=.75,col="red")+
  geom_path(aes(x=long,y=lat,group=group,order=order),data=fortify(worldmap),size=.5)+
  coord_equal()

It’s hard to see all the points, let’s bin them…

ggplot(st,aes(y=latitude,x=longitude)) +
  facet_wrap(~element)+
  stat_bin2d(bins=75)+
  scale_fill_gradient(low="yellow",high="red",trans="log",
                       breaks = c(1,10,100,1000))+
  geom_path(aes(x=long,y=lat,group=group,order=order),data=fortify(worldmap),size=.5)+
  coord_equal()

Your turn

Produce a binned map (like above) with the following modifications:

include only stations with data between 1950 and 2000
include only tmax

ggplot(filter(st,
              first_year<=1950 & 
              last_year>=2000 & 
              element=="TMAX"),
       aes(y=latitude,x=longitude)) +
  stat_bin2d(bins=75)+
  scale_fill_gradient(
    low="yellow",high="red",
    trans="log",
    breaks = c(1,10,50))+
  geom_path(aes(x=long,
        y=lat,group=group,
        order=order),
        data=fortify(worldmap),
        size=.5)+
  coord_equal()

Download daily data from GHCN

ghcnd() will download a .dly file for a particular station. But how to choose?

coords=as.matrix(geocode("Buffalo, NY"))
coords

##            lon      lat
## [1,] -78.88642 42.89606

dplyr::filter(st,
              grepl("BUFFALO",name)&
              between(latitude,coords[2]-1,coords[2]+1) &
              between(longitude,coords[1]-1,coords[1]+1)&
         element=="TMAX")

##            id latitude longitude elevation
## 1 USC00301010  42.8833  -78.8833    -999.9
## 2 USC00301018  42.9333  -78.9000     177.1
## 3 USW00014733  42.9486  -78.7369     211.2
##            name gsn_flag    wmo_id element
## 1    NY BUFFALO                       TMAX
## 2 NY BUFFALO #2                       TMAX
## 3    NY BUFFALO          HCN 72528    TMAX
##   first_year last_year
## 1       1873      1944
## 2       1937      1941
## 3       1938      2015

d=ghcnd("USW00014733", path = datadir)
d$data[1:5,1:12]

##            id year month element VALUE1 MFLAG1
## 1 USW00014733 1938     5    TMAX    144       
## 2 USW00014733 1938     5    TMIN     39       
## 3 USW00014733 1938     5    PRCP      0      T
## 4 USW00014733 1938     6    TMAX    261       
## 5 USW00014733 1938     6    TMIN    100       
##   QFLAG1 SFLAG1 VALUE2 MFLAG2 QFLAG2 SFLAG2
## 1             0    211                    0
## 2             0     83                    0
## 3             0      0      T             0
## 4             0    222                    0
## 5             0    133                    0

Could also filter using over() in sp package…

See CDO Description and raw GHCND metadata for more details.

Quality Control: MFLAG

Measurement Flag/Attribute

Blank no measurement information applicable
B precipitation total formed from two twelve-hour totals
H represents highest or lowest hourly temperature (TMAX or TMIN) or average of hourly values (TAVG)
K converted from knots
…

See CDO Description

Quality Control: QFLAG

Blank did not fail any quality assurance check
D failed duplicate check
G failed gap check
K failed streak/frequent-value check
N failed naught check
O failed climatological outlier check
S failed spatial consistency check
T failed temporal consistency check
W temperature too warm for snow
…

See CDO Description

Quality Control: SFLAG

Indicates the source of the data…

Reshape and clean data

rnoaa includes a function ghcnd_splitvars() for reshaping the original data.

d1=ghcnd_splitvars(d)
str(d1,list.len = 3)

## List of 46
##  $ tmax:Classes 'tbl_df', 'tbl' and 'data.frame':    28308 obs. of  6 variables:
##   ..$ id   : chr [1:28308] "USW00014733" "USW00014733" "USW00014733" "USW00014733" ...
##   ..$ tmax : int [1:28308] 144 261 189 256 183 139 144 0 56 11 ...
##   ..$ date : Date[1:28308], format:  ...
##   .. [list output truncated]
##  $ tmin:Classes 'tbl_df', 'tbl' and 'data.frame':    28308 obs. of  6 variables:
##   ..$ id   : chr [1:28308] "USW00014733" "USW00014733" "USW00014733" "USW00014733" ...
##   ..$ tmin : int [1:28308] 39 100 156 194 94 50 -6 -61 -94 -111 ...
##   ..$ date : Date[1:28308], format:  ...
##   .. [list output truncated]
##  $ prcp:Classes 'tbl_df', 'tbl' and 'data.frame':    28308 obs. of  6 variables:
##   ..$ id   : chr [1:28308] "USW00014733" "USW00014733" "USW00014733" "USW00014733" ...
##   ..$ prcp : int [1:28308] 0 0 5 295 97 0 0 0 48 15 ...
##   ..$ date : Date[1:28308], format:  ...
##   .. [list output truncated]
##   [list output truncated]

Merge datasets

d2=lapply(1:length(d1),function(i) {
  x=d1[[i]]
  ivar=names(d1)[i]
  x$var=ivar
  x$val=unlist(x[,ivar])
  return(select(x,-contains(ivar)))})%>%
  bind_rows()
head(d2)

## Source: local data frame [6 x 7]
## 
##            id       date mflag qflag sflag   var
##         (chr)     (date) (chr) (chr) (chr) (chr)
## 1 USW00014733 1938-05-01                 0  tmax
## 2 USW00014733 1938-06-01                 0  tmax
## 3 USW00014733 1938-07-01                 0  tmax
## 4 USW00014733 1938-08-01                 0  tmax
## 5 USW00014733 1938-09-01                 0  tmax
## 6 USW00014733 1938-10-01                 0  tmax
## Variables not shown: val (int)

Filter with QC data and change units

d3=d2%>%
  filter( 
    qflag==" " &
    var%in%c("tmax","tmin","prcp"))%>%                            # drop bad QC values
  mutate(val=ifelse(val==-9999,NA,val))%>%
  mutate(val=ifelse(var%in%c("tmax","tmin"), val/10,val))%>%  # convert to degrees C
  arrange(var,date)

Summarize QC flags

Summarize the QC flags. How many of which type are there? Should we be more conservative?

table(d3$mflag,d3$var)

##    
##      prcp  tmax  tmin
##     23149 28296 28295
##   T  5158     0     0

T failed temporal consistency check

Reshape by variable for easy plotting

use reshape2::dcast() for flexible reshaping of data sets.

d3b=dcast(d3,id+date~var,value.var="val")%>%
  arrange(date)%>%
  na.omit() 
head(d3b)

##            id       date prcp tmax tmin
## 1 USW00014733 1938-05-01    0 14.4  3.9
## 2 USW00014733 1938-05-02    0 21.1  8.3
## 3 USW00014733 1938-05-03   25 16.7  7.2
## 4 USW00014733 1938-05-04  112 20.6  9.4
## 5 USW00014733 1938-05-05    0 31.1 10.6
## 6 USW00014733 1938-05-06   64 19.4  7.8

Use melt() to undo a cast()

Plot temperatures

ggplot(d3b,
       aes(ymax=tmax,ymin=tmin,x=date))+
  geom_ribbon(col="grey")+
  geom_line(aes(y=(tmax+tmin)/2),col="red")

Limit to a few years.

ggplot(filter(d3b,date>as.Date("2014-01-01")),
       aes(ymax=tmax,ymin=tmin,x=date))+
  geom_ribbon(col="grey")+
  geom_line(aes(y=(tmax+tmin)/2),col="red")

Zoo package for rolling functions

Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations)

rollmean(): Rolling mean
rollsum(): Rolling sum
rollapply(): Custom functions

Use rollmean to calculate a rolling 60-day average.

align whether the index of the result should be left- or right-aligned or centered

d4 = d3b %>% 
  arrange(date) %>%
  mutate(tmax.60 = rollmean(x = tmax, 60, align = "center", fill = NA),
         tmax.b60 = rollmean(x = tmax, 60, align = "right", fill = NA))

ggplot(filter(d4,date>as.Date("2014-01-01")),
       aes(ymax=tmax,ymin=tmin,x=date))+
  geom_ribbon(fill="grey")+
  geom_line(aes(y=(tmin+tmax)/2),col=grey(0.4),size=.5)+
  geom_line(aes(y=tmax.60),col="red")+
  geom_line(aes(y=tmax.b60),col="darkred")

## Warning: Removed 30 rows containing missing values
## (geom_path).

Your Turn

Plot a 30-day rolling “right” aligned sum of precipitation.

tp=d3b %>%
  arrange(date)  %>% 
  mutate(prcp.30 = rollsum(x = prcp, 30, align = "right", fill = NA))

ggplot(tp,aes(y=prcp,x=date))+
  geom_line(aes(y=prcp.30),col="black")+ 
  geom_line(col="red")

## Warning: Removed 29 rows containing missing values
## (geom_path).

Time Series analysis

Most timeseries functions use the time series class (ts)

tmin.ts=ts(d3b$tmin,deltat=1/365)

Temporal autocorrelation

Values are highly correlated!

ggplot(d3b,aes(y=tmax,x=lag(tmax)))+
  geom_point()+
  geom_abline(intercept=0, slope=1)

## Warning: Removed 1 rows containing missing values
## (geom_point).

Autocorrelation functions

autocorrelation \(x\) vs. \(x_{t-1}\) (lag=1)
partial autocorrelation. \(x\) vs. \(x_{n}\) after controlling for correlations \(\in t-1:n\)

Autocorrelation

acf(tmin.ts,lag.max = 365*3)

Partial Autocorrelation

pacf(tmin.ts,lag.max = 365*3)

Seasonal decomposition

Decompose data into seasonal, trend, and remaining components.

seasd = stl(tmin.ts,"periodic",t.window=365*10)
plot(seasd)

Your Turn

Compute the seasonal decomposition for precipitation

prcp.ts=ts(d3b$prcp,deltat=1/365) 
pseasd = stl(prcp.ts,"periodic",t.window=365*10)
plot(pseasd)

Checking for significant trends

Compute temporal aggregation indices

Group by month, season, year, and decade.

How to convert years into ‘decades’?

## [1] 1938

round(1938,-1)

## [1] 1940

floor(1938/10)*10

## [1] 1930

d5=d4%>%
  mutate(month=as.numeric(format(date,"%m")),
        year=as.numeric(format(date,"%Y")),
        season=ifelse(month%in%c(12,1,2),"Winter",
            ifelse(month%in%c(3,4,5),"Spring",
              ifelse(month%in%c(6,7,8),"Summer",
                ifelse(month%in%c(9,10,11),"Fall",NA)))),
        dec=(floor(as.numeric(format(date,"%Y"))/10)*10))
head(d5)

##            id       date prcp tmax tmin tmax.60
## 1 USW00014733 1938-05-01    0 14.4  3.9      NA
## 2 USW00014733 1938-05-02    0 21.1  8.3      NA
## 3 USW00014733 1938-05-03   25 16.7  7.2      NA
## 4 USW00014733 1938-05-04  112 20.6  9.4      NA
## 5 USW00014733 1938-05-05    0 31.1 10.6      NA
## 6 USW00014733 1938-05-06   64 19.4  7.8      NA
##   tmax.b60 month year season  dec
## 1       NA     5 1938 Spring 1930
## 2       NA     5 1938 Spring 1930
## 3       NA     5 1938 Spring 1930
## 4       NA     5 1938 Spring 1930
## 5       NA     5 1938 Spring 1930
## 6       NA     5 1938 Spring 1930

Timeseries models

How to assess change? Simple differences?

d5%>%
  mutate(period=ifelse(year<=1976-01-01,"early","late"))%>%
  group_by(period)%>%
  summarize(n=n(),tmin=mean(tmin),tmax=mean(tmax),prcp=mean(prcp))

## Source: local data frame [2 x 5]
## 
##   period     n     tmin     tmax     prcp
##    (chr) (int)    (dbl)    (dbl)    (dbl)
## 1  early 13392 4.204518 13.65579 26.04017
## 2   late 14911 4.689504 13.67454 28.52344

Maximum Temperature

library(EnvStats)

seasonal=d5%>%
  group_by(year,season)%>%
  summarize(n=n(),tmin=mean(tmin),tmax=mean(tmax),prcp=mean(prcp))%>%
  filter(n>75)

ggplot(seasonal,aes(y=tmin,x=year))+
  facet_wrap(~season,scales = "free_y")+
  stat_smooth(method="lm", se=T)+
  geom_line()

Kendal Seasonal Trend Test

Nonparametric seasonal trend analysis.

e.g. Hirsch-Slack test

t1=kendallSeasonalTrendTest(tmax~season+year,data=seasonal)
t1

## 
## Results of Hypothesis Test
## --------------------------
## 
## Null Hypothesis:                 All 4 values of tau = 0
## 
## Alternative Hypothesis:          The seasonal taus are not all equal
##                                  (Chi-Square Heterogeneity Test)
##                                  At least one seasonal tau != 0
##                                  and all non-zero tau's have the
##                                  same sign (z Trend Test)
## 
## Test Name:                       Seasonal Kendall Test for Trend
##                                  (with continuity correction)
## 
## Estimated Parameter(s):          tau       = -0.0010059683
##                                  slope     = -0.0001630435
##                                  intercept =  7.2593331244
## 
## Estimation Method:               tau:        Weighted Average of
##                                              Seasonal Estimates
##                                  slope:      Hirsch et al.'s
##                                              Modification of
##                                              Thiel/Sen Estimator
##                                  intercept:  Median of
##                                              Seasonal Estimates
## 
## Data:                            y      = tmax  
##                                  season = season
##                                  year   = year  
## 
## Data Source:                     seasonal
## 
## Sample Sizes:                    Fall   =  77
##                                  Summer =  78
##                                  Spring =  77
##                                  Winter =  76
##                                  Total  = 308
## 
## Test Statistics:                 Chi-Square (Het) =  7.32260444
##                                  z (Trend)        = -0.03957982
## 
## Test Statistic Parameter:        df = 3
## 
## P-values:                        Chi-Square (Het) = 0.06229604
##                                  z (Trend)        = 0.96842812
## 
## Confidence Interval for:         slope
## 
## Confidence Interval Method:      Gilbert's Modification of
##                                  Theil/Sen Method
## 
## Confidence Interval Type:        two-sided
## 
## Confidence Level:                95%
## 
## Confidence Interval:             LCL = -0.006674135
##                                  UCL =  0.006483909

Minimum Temperature

t2=kendallSeasonalTrendTest(tmin~season+year,data=seasonal)
t2

## 
## Results of Hypothesis Test
## --------------------------
## 
## Null Hypothesis:                 All 4 values of tau = 0
## 
## Alternative Hypothesis:          The seasonal taus are not all equal
##                                  (Chi-Square Heterogeneity Test)
##                                  At least one seasonal tau != 0
##                                  and all non-zero tau's have the
##                                  same sign (z Trend Test)
## 
## Test Name:                       Seasonal Kendall Test for Trend
##                                  (with continuity correction)
## 
## Estimated Parameter(s):          tau       =   0.19027701
##                                  slope     =   0.01535573
##                                  intercept = -27.30584760
## 
## Estimation Method:               tau:        Weighted Average of
##                                              Seasonal Estimates
##                                  slope:      Hirsch et al.'s
##                                              Modification of
##                                              Thiel/Sen Estimator
##                                  intercept:  Median of
##                                              Seasonal Estimates
## 
## Data:                            y      = tmin  
##                                  season = season
##                                  year   = year  
## 
## Data Source:                     seasonal
## 
## Sample Sizes:                    Fall   =  77
##                                  Summer =  78
##                                  Spring =  77
##                                  Winter =  76
##                                  Total  = 308
## 
## Test Statistics:                 Chi-Square (Het) = 5.336492
##                                  z (Trend)        = 4.914530
## 
## Test Statistic Parameter:        df = 3
## 
## P-values:                        Chi-Square (Het) = 1.487519e-01
##                                  z (Trend)        = 8.899585e-07
## 
## Confidence Interval for:         slope
## 
## Confidence Interval Method:      Gilbert's Modification of
##                                  Theil/Sen Method
## 
## Confidence Interval Type:        two-sided
## 
## Confidence Level:                95%
## 
## Confidence Interval:             LCL = 0.009473889
##                                  UCL = 0.021066182

Autoregressive models

See Time Series Analysis Task View for summary of available packages/models.

Moving average (MA) models
autoregressive (AR) models
autoregressive moving average (ARMA) models
frequency analysis
Many, many more…

Climate Metrics

Climdex indices

ClimDex

Format data for `climdex`

library(PCICt)
    ## Parse the dates into PCICt.
    pc.dates <- as.PCICt(as.POSIXct(d5$date),cal="gregorian")

Generate the climdex object

  library(climdex.pcic)
    ci <- climdexInput.raw(
      tmax=d5$tmax,tmin=d5$tmin,prec=d5$prcp,
      pc.dates,pc.dates,pc.dates, 
      base.range=c(1971, 2000))

Cumulative dry days

cdd= climdex.cdd(ci, spells.can.span.years = TRUE)
plot(cdd~as.numeric(names(cdd)),type="l")

Diurnal Temperature Range

dtr=climdex.dtr(ci, freq = c("annual"))
plot(dtr,type="l")

Frost Days

fd=climdex.fd(ci)
plot(fd,type="l")

Your Turn

See all available indices with:

climdex.get.available.indices(ci)

##  [1] "climdex.su"      "climdex.id"     
##  [3] "climdex.txx"     "climdex.txn"    
##  [5] "climdex.tx10p"   "climdex.tx90p"  
##  [7] "climdex.wsdi"    "climdex.fd"     
##  [9] "climdex.tr"      "climdex.tnx"    
## [11] "climdex.tnn"     "climdex.tn10p"  
## [13] "climdex.tn90p"   "climdex.csdi"   
## [15] "climdex.rx1day"  "climdex.rx5day" 
## [17] "climdex.sdii"    "climdex.r10mm"  
## [19] "climdex.r20mm"   "climdex.rnnmm"  
## [21] "climdex.cdd"     "climdex.cwd"    
## [23] "climdex.r95ptot" "climdex.r99ptot"
## [25] "climdex.prcptot" "climdex.gsl"    
## [27] "climdex.dtr"

Select 3 indices and use the kendallSeasonalTrendTest() to assess trends over the available data period.

dtr=climdex.dtr(ci,freq = "annual")
plot(dtr,type="l")

dtr=climdex.dtr(ci,freq = "monthly")
plot(dtr,type="l")

dtrf=as.data.frame(dtr)
dtrf$date=as.Date(paste0(
  rownames(dtrf),"-15"),"%Y-%m-%d")
dtrf$month=as.numeric(format(dtrf$date,"%m"))
dtrf$year=as.numeric(format(dtrf$date,"%Y"))

kendallSeasonalTrendTest(
  dtr~month+year,data=dtrf)

## 
## Results of Hypothesis Test
## --------------------------
## 
## Null Hypothesis:                 All 12 values of tau = 0
## 
## Alternative Hypothesis:          The seasonal taus are not all equal
##                                  (Chi-Square Heterogeneity Test)
##                                  At least one seasonal tau != 0
##                                  and all non-zero tau's have the
##                                  same sign (z Trend Test)
## 
## Test Name:                       Seasonal Kendall Test for Trend
##                                  (with continuity correction)
## 
## Estimated Parameter(s):          tau       = -0.1844224
##                                  slope     = -0.0130303
##                                  intercept = 24.3858349
## 
## Estimation Method:               tau:        Weighted Average of
##                                              Seasonal Estimates
##                                  slope:      Hirsch et al.'s
##                                              Modification of
##                                              Thiel/Sen Estimator
##                                  intercept:  Median of
##                                              Seasonal Estimates
## 
## Data:                            y      = dtr  
##                                  season = month
##                                  year   = year 
## 
## Data Source:                     dtrf
## 
## Sample Sizes:                    1     =  77
##                                  2     =  77
##                                  3     =  77
##                                  4     =  77
##                                  5     =  78
##                                  6     =  78
##                                  7     =  78
##                                  8     =  78
##                                  9     =  78
##                                  10    =  77
##                                  11    =  77
##                                  12    =  77
##                                  Total = 929
## 
## Number NA/NaN/Inf's:             7
## 
## Test Statistics:                 Chi-Square (Het) = 71.471604
##                                  z (Trend)        = -8.284672
## 
## Test Statistic Parameter:        df = 11
## 
## P-values:                        Chi-Square (Het) = 6.409606e-11
##                                  z (Trend)        = 1.184350e-16
## 
## Confidence Interval for:         slope
## 
## Confidence Interval Method:      Gilbert's Modification of
##                                  Theil/Sen Method
## 
## Confidence Interval Type:        two-sided
## 
## Confidence Level:                95%
## 
## Confidence Interval:             LCL = -0.01606296
##                                  UCL = -0.01010091

Climate Metrics from daily weather data

Today

Climate Extremes

Climate Metrics: ClimdEX

27 Core indices

Weather Data

Climate Data Online

GHCN

Options for downloading data

FedData package

NOAA API

rNOAA package

Libraries

Station locations

GHCND Variables

filter() to temperature and precipitation

Map GHCND stations

Your turn

Download daily data from GHCN

Quality Control: MFLAG

Quality Control: QFLAG

Quality Control: SFLAG

Reshape and clean data

Merge datasets

Filter with QC data and change units

Summarize QC flags

Reshape by variable for easy plotting

Zoo package for rolling functions

Your Turn

Time Series analysis

Temporal autocorrelation

Autocorrelation functions

Autocorrelation

Partial Autocorrelation

Seasonal decomposition

Your Turn

Checking for significant trends

Compute temporal aggregation indices

Group by month, season, year, and decade.

Timeseries models

Maximum Temperature

Kendal Seasonal Trend Test

Minimum Temperature

Autoregressive models

Climate Metrics

Climdex indices

Format data for climdex

Generate the climdex object

Cumulative dry days

Diurnal Temperature Range

Frost Days

Your Turn

`FedData` package

`rNOAA` package

`filter()` to temperature and precipitation

Format data for `climdex`